MétaCan
Menu
Back to cohort
Record W2498973890 · doi:10.1057/9781137363909_5

‘The Lasses Are Massing’: The Land Army in England and Wales

2014· book-chapter· en· W2498973890 on OpenAlexaff
Bonnie White

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHistorical Gender and Feminism Studies
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsRealmAgricultureResizingWork (physics)Rural areaGeographyWorld War IIEconomic growthPolitical scienceEconomic historyEconomySocioeconomicsHistoryBusinessEconomicsEconomic policyArchaeologyLawEngineeringEuropean union

Abstract

fetched live from OpenAlex

Throughout the twentieth century there was a significant decline in the number of women employed in agriculture in Britain and although the WLA temporarily replenished their numbers, the transitory nature of the organisation was part of a larger trend in agriculture that both preceded and followed the war. The loss of women from the countryside in the late nineteenth and early twentieth centuries was the result of the undervaluation of women in the farming industry and the belief that women could find better employment opportunities in the towns and cities. 1 The loss of women from the land was especially prevalent in areas where primogeniture persisted. 2 The decreasing number of women employed in farming does not mean that women ceased to play an integral part in farm life. In the early twentieth century family farms were labour intensive, and with a shrinking domestic market and agricultural labour force, hiring outsiders was unprofitable. With decreased hiring of both men and women, the farmer’s female relatives were called upon to fill the labour gap. 3 Their work, however, was constrained to a narrow range of jobs, including feeding animals, caring for the household and children, and operating some machinery that had traditionally been operated by men. While the work of female relatives expanded in the early twentieth century, it typically remained within the realm of female domestic duties. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.008
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.260
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooksSame topicHistorical Gender and Feminism StudiesFrench-language works237,207